AI Systems Governance and Readiness Consulting
Cyberonix supports boards, audit committees, Chief AI Officers, and Chief Risk Officers establishing or maturing the governance program that sits above any individual AI system. An AI systems governance engagement at this level is organization-level rather than system-level: the AI policy layer, lifecycle and MLOps maturity, third-party AI tool review, incident response, and regulatory readiness are evaluated as a program rather than evaluated system by system. The unit of analysis is the program as a whole: how the organization decides which AI systems to build or buy, how it controls them across their lifecycles, and how it answers to a sophisticated audit or regulator. The deliverable is a written assessment of the organization’s AI governance posture together with a roadmap for closing the gaps the same audit or regulator would surface, prioritized by exposure and by the dependencies between fixes.
AI Policy and Lifecycle Controls
The policy layer governs every downstream AI deployment, and gaps in it propagate into every system the program produces.
The policy layer is evaluated against the scope it actually covers: which AI use cases are in scope and which are excluded, what approval and gating practices apply to new AI systems and new vendor tools before they enter production, what model-lifecycle controls the policy imposes across development, evaluation, deployment, monitoring, and retirement, and what data-handling controls apply at training time and at inference time. Each of these is read against the AI activity the organization is actually conducting, not against a generic policy template.
Accountability for each control is examined alongside the control itself. The assessment names where decisions are required for each policy, what approval each control demands before a deployment can proceed, and whether the documented accountability holds when a deployment decision is contested in practice rather than only on paper.
The deliverable names what is in place, what is documented but not enforced when tested against an actual deployment decision, and what is missing relative to the organization’s risk profile and the AI activity it conducts. Gaps are reported with the artifact that would close them: a policy clause, a documented approval gate, or a control the organization does not yet operate.
MLOps Maturity and Third-Party AI Tool Review
Technical infrastructure for AI governance is evaluated against the controls the policy layer requires, not against a generic MLOps maturity model.
In-house infrastructure is examined first: the model registry that records what is in production and under what version, the evaluation pipelines that gate promotion from development through staging into production, the monitoring and drift-detection signals that surface performance degradation in deployed systems, the incident-response runbooks that govern how the organization reacts to model failure, the audit logging that supports later reconstruction of a model decision, and the reproducibility of training and inference. Each is evaluated against what the policy layer requires the organization to do and what the deployed systems actually need.
Systems built on third-party foundation models or AI-as-a-service vendors are evaluated under a different control set. The organization does not have the same visibility into training data, evaluation procedure, or model internals, and governance shifts from direct evaluation to vendor risk review, contractual control over change and disclosure, and proxy monitoring conducted on the inference surface the vendor exposes. The assessment names where each production system sits on this spectrum and what the governance gap looks like for each one.
The vendor evaluation methodology Cyberonix applies covers the model-card and system-card disclosures the vendor publishes, the adequacy of the evaluation evidence those disclosures rest on, the vendor’s security and data-handling posture, and the incident-response and change-management commitments the contract supports. Where critical AI capability sits with a vendor whose disclosures or commitments fall short of the organization’s risk profile, the gap is named and the remediation options are scoped.
Regulatory Readiness and Audit Posture
Regulatory readiness is the gap between the organization’s existing AI governance program and the obligations the applicable regulatory frameworks impose on it.
At the organization level, the NIST AI Risk Management Framework Govern function is the primary frame: the policies, accountability structures, and culture that the framework’s Map, Measure, and Manage functions presuppose. The EU AI Act provider and deployer obligations are evaluated against each AI system in scope according to whether the organization is acting as provider, deployer, or both, including the obligations that attach specifically to general-purpose AI use within the organization. ISO/IEC 42001 alignment is examined where the organization is pursuing or considering management-system certification for its AI program.
Sector-specific obligations are added where the industry context invokes them. Financial-services model risk management under the interagency SR 26-2 framework that superseded SR 11-7 and OCC 2011-12 in April 2026, healthcare AI controls under the regulatory regime governing the organization’s clinical or operational deployments, and HR AI controls under the federal and state frameworks that apply to employment decision-making are cited where the organization’s deployed systems fall within their scope. SR 26-2 is most relevant to banking organizations with over $30 billion in total assets, and its model guidance does not extend to generative and agentic AI models, leaving those systems to the organization’s own risk management and governance practices. An unrelated framework is not added to fill space.
The deliverable names the gaps between the existing program and the obligations applicable to the organization, and the order in which they should be closed. The findings are tied to the artifacts that support them, in the same documentation register the firm applies in its litigation work, calibrated here to a leadership audience rather than to counsel and the court.
Our Experts
The Cyberonix AI governance team is the same group of senior consultants who staff the firm’s litigation work. Each holds a faculty appointment at a research university in the United States, with recognitions including IEEE Fellow status, ACM Distinguished Member status, and named professorships at leading research institutions. Industry depth spans AI systems engineering and AI governance: model development and evaluation, MLOps infrastructure, third-party AI tool review, and the regulatory frameworks that govern an enterprise AI program. Engagements are staffed so the lead consultant’s research record and industry background align with the AI governance program under review.